In this paper, we study an alternative approach to the V2V resource scheduling in the out-of-coverage (OOC) zones. We propose a centralized scheduler residing in the network, comprising a reinforcement learning (RL) agent that pre-assigns resources to the vehicles for their V2V transmissions in an OOC area, before the vehicles enter such an area.  We are motivated to use an RL-based approach due to its successful applications for resource allocation problems in general [1]}, and for vehicular communications in particular [2]}. The scheduler, which we call VRLS (Vehicular Reinforcement Learning Scheduler), was proposed and demonstrated to be potentially efficient in [3]}. VRLS design addresses two practical challenges. First, training and deploying RL solutions in the real world is costly in terms of training. In particular:
